期刊：
Multimedia Tools and Applications,2023年82(9):14091-14105 ISSN：1380-7501
通讯作者：
Shixin Peng
作者机构：
[Peng, Shixin; Tan, Lei; Chen, Chang; Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
通讯机构：
[Shixin Peng] N;National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
关键词：
Person re-identification;Cross modality;Channel decoupling
摘要：
Cross-modality person re-identification (CM-ReID) is a very challenging problem due to the discrepancy in data distributions between visible and near-infrared modalities. To obtain a robust sharing feature representation, existing methods mainly focus on image generation or feature constrain to decrease the modality discrepancy, which ignores the large gap between mixed-spectral visible images and single-spectral near-infrared images. In this paper, we address the problem by decoupling the mixed-spectral visible images into three single-spectral subspaces R, G, and B. By aligning the spectrum, we noted that even using a single spectral image instead of the VIS images could result in a better performance. Based on the above observation, we further introduce a clear and effective three-path channel decoupling network (CDNet) for combining the three spectral images. Extensive experiments implemented on the benchmark CM-ReID datasets, SYSU-MM01 and RegDB indicated that our method achieved state-of-the-art performance and outperformed existing approaches by a large margin. On the RegDB dataset, the absolute gain of our method in terms of rank-1 and mAP is well over 15.4% and 8.5%, respectively, compared with the state-of-the-art methods.
作者：
Su, Zhu;Li, Yue;Liu, Zhi;Sun, Jianwen;Yang, Zongkai;...
期刊：
ETR&D-EDUCATIONAL TECHNOLOGY RESEARCH AND DEVELOPMENT,2023年71(5):1941-1963 ISSN：1042-1629
通讯作者：
Liu, S
作者机构：
[Yang, Zongkai; Liu, Sannyuya; Liu, S; Liu, Zhi; Su, Zhu; Li, Yue; Sun, Jianwen] Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.;[Yang, Zongkai; Liu, Sannyuya; Liu, S] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.;[Yang, Zongkai; Liu, Sannyuya; Liu, S; Liu, Zhi; Su, Zhu; Li, Yue; Sun, Jianwen] Cent China Normal Univ, Fac Artificial Intelligence Educ, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Liu, S ] C;Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.;Cent China Normal Univ, Fac Artificial Intelligence Educ, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
摘要：
In recent years, cross-modal hashing has attracted an increasing attention due to its fast retrieval speed and low storage requirements. However, labeled datasets are limited in real application, and existing unsupervised cross-modal hashing algorithms usually employ heuristic geometric prior as semantics, which introduces serious deviations as the similarity score from original features cannot reasonably represent the relationships among instances. In this paper, we study the unsupervised deep cross-modal hash retrieval method and propose a novel Semantic Graph Evolutionary Hashing (SGEH) to solve the above problem. The key novelty of SGEH is its evolutionary affinity graph construction method. To be concrete, we explore the sparse similarity graph with clustering results, which evolve from fusing the affinity information from code-driven graph on intrinsic data and subsequently extends to dense hybrid semantic graph which restricts the process of hash code learning to learn more discriminative results. Moreover, the batch-inputs are chosen from edge set rather than vertexes for better exploring the original spatial information in the sparse graph. Experiments on four benchmark datasets demonstrate the superiority of our framework over the state-of-the-art unsupervised cross-modal retrieval methods. Code is available at: https://github.com/theusernamealreadyexists/SGEH.
期刊：
IEEE Transactions on Industrial Informatics,2023年:1-11 ISSN：1551-3203
通讯作者：
Yang, B;Liu, H
作者机构：
[Yang, Bing; Liu, Tingting] Hubei Univ, Sch Educ, 368 Youyi Rd, Wuhan 430062, Hubei, Peoples R China.;[Yang, Bing; Liu, Tingting] City Univ Hong Kong, Dept Mech Engn, Kowloon, Hong Kong, Peoples R China.;[Zhang, Zhaoli; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
通讯机构：
[Yang, B ] H;[Liu, H ] C;Hubei Univ, Sch Educ, 368 Youyi Rd, Wuhan 430062, Hubei, Peoples R China.;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
摘要：
2D Human pose estimation (HPE) has been widely used in the many fields such as behavioral understanding, identity authentication, and industrial automatic manufacturing. Most of the previous studies have encountered many constraints, such as restricted scenarios and strict inputs. To solve this problem, we present a simple yet effective HPE network called limb direction cues-aware network (LDCNet) with limb direction cues and differentiated Cauchy labels, which can efficiently suppress uncertainties and prevent deep networks from over-fitting uncertain keypoint positions. In particular, LDCNet suppresses the uncertainties from two aspects. (1) A differentiated Cauchy coordinate encoding method is designed to reveal the limb direction information among adjacent keypoints. (2) Jeffreys divergence is introduced as loss function to measure the prediction heatmap and ground-truth one. Positions of keypoints are perceived at the limb direction based deep network in an end-to-end manner. An extensive study on two benchmark data sets (i.e., MS COCO and MPII) illustrates the superiority of the proposed LDCNet model over state- of-the-art approaches.
期刊：
Journal of King Saud University - Computer and Information Sciences,2023年35(7):101594 ISSN：1319-1578
通讯作者：
Li, H
作者机构：
[Yang, Shuoqiu; Li, H; Li, Hao; Du, Xu; Wang, Jing] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Hung, Jui-Long] Cent China Normal Univ, Natl Engn Lab Educationgal Big Data, Wuhan 430079, Peoples R China.;[Hung, Jui-Long] Boise State Univ, Dept Educ Technol, Boise, ID 83725 USA.
通讯机构：
[Li, H ] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
摘要：
Quiz question annotation aims to assign the most relevant knowledge point to a question, which is a key technology to support intelligent education applications. However, the existing methods only extract the explicit semantic information that reveals the literal meaning of a question, and ignore the implicit knowledge information that highlights the knowledge intention. To this end, an innovative dual-channel model, the Semantic-Knowledge Mapping Network (S-KMN) is proposed to enrich the question representation from two perspectives, semantic and knowledge, simultaneously. It integrates semantic features learning and knowledge mapping network (KMN) to extract explicit semantic features and implicit knowledge features of questions,respectively. Designing KMN to extract implicit knowledge features is the focus of this study. First, the context-aware and sequence information of knowledge attribute words in the question text is integrated into the knowledge attribute graph to form the knowledge representation of each question. Second, learning a projection matrix, which maps the knowledge representation to the latent knowledge space based on the scene base vectors, and the weighted summations of these base vectors serve as knowledge features. To enrich the question representation, an attention mechanism is introduced to fuse explicit semantic features and implicit knowledge features, which real-izes further cognitive processing on the basis of understanding semantics. The experimental results on 19,410 real-world physics quiz questions in 30 knowledge points demonstrate that the S-KMN outperforms the state-of-the-art text classification-based question annotation method. Comprehensive analysis and ablation studies validate the superiority of our model in selecting knowledge-specific features.(c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
作者：
Yang, He;Cai, Jin;Yang, Harrison Hao;Wang, Xiaochen
期刊：
Journal of Computing in Higher Education,2023年35(1):126-143 ISSN：1042-1726
通讯作者：
Harrison Hao Yang
作者机构：
[Cai, Jin; Yang, He] Hubei Univ Educ, Sch Comp, Wuhan 430205, Peoples R China.;[Yang, He] Cent China Normal Univ, Wuhan Huada Natl Elearning Technol Co Ltd, Wuhan 430079, Peoples R China.;[Yang, Harrison Hao] SUNY Coll Oswego, Sch Educ, 4060 Route 104, Oswego, NY 13126 USA.;[Yang, Harrison Hao] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan 430079, Peoples R China.;[Wang, Xiaochen] Capital Normal Univ, Coll Teacher Educ, Beijing 100091, Peoples R China.
通讯机构：
[Harrison Hao Yang] S;School of Education, State University of New York at Oswego, Oswego, USA<&wdkj&>National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
期刊：
Expert Systems with Applications,2023年217:119511 ISSN：0957-4174
通讯作者：
Zengzhao Chen
作者机构：
[Lu, Yuanyuan; Chen, Zengzhao; Li, Jiawen; Zheng, Qiuyu; Liu, Hai] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.;[Lu, Yuanyuan; Li, Jiawen; Zheng, Qiuyu] Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan 430079, Peoples R China.;[Chen, Zengzhao; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan 430079, Peoples R China.;[Liu, Tingting] Hubei Univ, Sch Educ, Wuhan 430062, Peoples R China.
通讯机构：
[Zengzhao Chen] F;Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China<&wdkj&>National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China
摘要：
Speaker embeddings have become the most popular feature representation in speaker verification. Improving the robustness of speaker embedding extraction systems is a crucial problem. A multi-scale residual aggregation network (MSRANet), which is a simple but efficient network with triplet input and triplet loss, is proposed in this paper. Two different aggregation strategies are utilized in frame-level feature extractors to capture long-term variations in speaker characteristics. Attention mechanism is employed to filter a large number of parameters in temporal and frequency dimensions, which can effectively focus on the significant information and neglect the redundancy feature. Extensive experiments on the VoxCeleb1 and VoxCeleb2 wild datasets are also conducted to evaluate the performance of the proposed method. In comparison with four baselines experiments, obtained results demonstrate that the proposed MSRANet achieves a state-of-the-art performance of an equal error rate of 3.84% and an accuracy rate of 98.76%. Furthermore, the proposed method is proven to be effective in cross-scenarios adaptability through training performance on the LibriSpeech dataset. The proposed MSRANet has an equal error rate of 2.64% and an accuracy rate of 99.20% on LibriSpeech.
作者机构：
[Zhu, Songkai; Shen, Xiaoxuan; He, Xiuling; Fang, Jing; Li, Yangyang] Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan 430079, Peoples R China.;[Shen, Xiaoxuan; He, Xiuling; Fang, Jing; Li, Yangyang] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan 430079, Peoples R China.
通讯机构：
[Xiuling He] N;National Engineering Research Center of Educational Big Data, Central China Normal University, Wuhan, 430079, China<&wdkj&>National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, 430079, China
摘要：
Programming online judges (POJs) are widely used to train programming skills, and exercise recom-mendation algorithms in POJs have attracted wide attention. The current programming recommen-dation algorithms cannot make full use of the feedback of user-item pairs and cannot effectively express students' mastery of exercises. Therefore, we propose a dual-track feedback aggregation recommendation model for programming training (DTFARec). In this model, multiple types of feedback fusion mechanism (MTFFM) and dual-track method (DTM) are proposed to solve this problem and can better express students' mastery of exercises. The MTFFM uses an attention mechanism to learn different feedback information, and the DTM is able to fuse information from both feedback and interactive aspects. The experimental results on a real-world dataset show that the model has better recommendation performance than the best performing benchmark and that our method can effectively model students' mastery of exercises.(c) 2022 Elsevier B.V. All rights reserved.
期刊：
IEEE Systems, Man, and Cybernetics Magazine,2023年9(1):25-36 ISSN：2380-1298
作者机构：
[Yuxin Liu; Jinsong Gui] School of Computer Science and Engineering, Central South University, Changsha, China;[N. Xiong] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, Hu Bei Province, China
摘要：
There will exist a growing interest in deploying data-intensive and content-rich applications on mobile smart devices. Also, ultrareliable and low-latency communications will be the critical requirements for obtaining good quality of experience for users of smart devices. However, the existing cellular architectures hardly provide a rich and stable spectrum supply to support ultrareliable and low-latency communications. Although future wireless networks are expected to effectively exploit the terahertz frequency band, it is difficult to obtain stable, ultrareliable, and low-latency communications due to the immaturity of both propagation models and radio interface technologies in such a high-frequency band. Therefore, this article introduces cognitive network brokers based on a data-driven cognitive network architecture to integrate and make full use of various resources to provide good network services for users, including an engine for spectrum and device cognition and an engine for cognitive network service construction.
期刊：
Children and Youth Services Review,2023年149:106916 ISSN：0190-7409
通讯作者：
Yang, W
作者机构：
[Li, Miaoyun; Wu, Di; Yang, Xiao] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan, Hubei, Peoples R China.;[Yang, Wei; Wang, Meiqian] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan, Hubei, Peoples R China.;[Lu, Chun] Strateg Res Base Minist Educ, Res Ctr Sci & Technol Promoting Educ Innovat & Dev, Strateg Res Base, Wuhan, Hubei, Peoples R China.
通讯机构：
[Yang, W ] C;Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan, Hubei, Peoples R China.
